PAPIs Latam 2019

The first expert systems were aimed at informing people so they made better decisions and depended less on hard-to-find-harder-to-pay human specialists. Now, however, with the increasing refinement of techniques, hidden layers, and ubiquity of complex systems, results are often applied as they come. Humans are not good at making decisions under risk, and in Machine Learning applications they often don't even know they're taking a rough probability as an inexorable result. So, how may we design systems that help humans make choices with healthy doses of skepticism?